Generating Force Vectors from Projective Truncated Signed Distance Fields for Collision Avoidance and Haptic Feedback

Signed Distance Fields are a common surface representation method widely used for both 3D mapping and obstacle avoidance. While the former traditionally uses projective Truncated Signed Distance Fields (TSDF), the latter often requires a complete Euclidean Signed Distance Field (ESDF) representation of the environment. In this paper, we propose a unified system by combining both methods to generate force vectors to nearby obstacles from a TSDF-based 3D reconstruction. We introduce a new merging scheme to better capture the geometry of the object, with no post-processing requirements, and a way to increase the effective range of the system. Validation experiments demonstrate the accuracy of the force vector calculation by comparing it against an ideal simulated environment. The flexibility of the system is demonstrated by implementing a haptic feedback teleoperation setup, which is validated through a user study in a teleoperation task. Through this, it is shown that the proposed method provides a statistically significant improvement to the task. Finally, a brief description on future improvements to the system is presented.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10801739
Citations
1
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.33

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article

Generating Force Vectors from Projective Truncated Signed Distance Fields for Collision Avoidance and Haptic Feedback

Seongjin Bien, Abdeldjallil Naceri, Sami Haddadin, Luis Figueredo
1 citations
Robot Manipulation and Learning
0.33
article

Generating Force Vectors from Projective Truncated Signed Distance Fields for Collision Avoidance and Haptic Feedback

Seongjin Bien, Abdeldjallil Naceri, Sami Haddadin, Luis Figueredo
article en
1 citations

Abstract

Signed Distance Fields are a common surface representation method widely used for both 3D mapping and obstacle avoidance. While the former traditionally uses projective Truncated Signed Distance Fields (TSDF), the latter often requires a complete Euclidean Signed Distance Field (ESDF) representation of the environment. In this paper, we propose a unified system by combining both methods to generate force vectors to nearby obstacles from a TSDF-based 3D reconstruction. We introduce a new merging scheme to better capture the geometry of the object, with no post-processing requirements, and a way to increase the effective range of the system. Validation experiments demonstrate the accuracy of the force vector calculation by comparing it against an ideal simulated environment. The flexibility of the system is demonstrated by implementing a haptic feedback teleoperation setup, which is validated through a user study in a teleoperation task. Through this, it is shown that the proposed method provides a statistically significant improvement to the task. Finally, a brief description on future improvements to the system is presented.

University of Nottingham (GB)
Bayer
Climate action
Openalex Percentile: Top 38%
Robot Manipulation and Learning
0.33
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